library("knitr") #knitr::opts_chunk$set(eval = FALSE)

**Please note:** This document reflects work in progress and will be updated from time to time. The most recent copy can be downloaded from https://tpetzoldt.github.io/growthrates/doc/Introduction.html

The growth rate of a population is a direct measure of
fitness. Therefore, determination of growth rates is common in many
disciplines of theoretical and applied biology, e.g. physiology,
ecology, eco-toxicology or pharmacology.
This package aims to streamline estimation of growth rates from direct
or indirect measures of population density (e.g. cell counts, optical
density or fluorescence) determined in batch experiments or field
observations. It should be applicable to different species of
bacteria, archaea, protists, and metazoa, e.g. *E. coli*,
*Cyanobacteria*, *Paramecium*, green algae or *Daphnia*.

The determination of growth rates from chemostat and semi-continuous cultures is currently not covered by the package, but we are open to include it, depending on your interest and the availability of data. The package is still under development and feedback is highly welcome.

The package includes three types of methods:

- Nonlinear fitting of parametric growth models like the logistic or
the Gompertz growth model. Parametric model fitting is done by using
package
`FME`

(Flexible Modelling Environment) of @Soetaert2010. In addition to growth models given in closed form (i.e. empirical regression equations or analytical solutions of differential equations) it is also possible to use numerically integrated systems of differential equation. Such models are then solved with package `deSolve' [@deSolve_jss]. - Fitting of linear models to the period of exponential growth using the ``growth rates made easy method'' of @Hall2014 ,
- Nonparametric growthrate estimation by using smoothers. R contains
several powerful smoothing methods, that can leveraged for this
purpose. The currently implemented method uses function
`smooth.spline`

, similar to the package`grofit`

[@Kahm2010].

The package contains methods to fit single data sets or complete
series of data sets organized in a data frame. It contains also
functions for extracting results (e.g. `coef`

, `summary`

, `deviance`

,
`obs`

, `residuals`

, `rsquared`

and `results`

) and methods for plotting
(`plot`

, `lines`

). The implementation follows an object oriented
style, so that the functions above determine automatically which
method is used for a given class of objects.

The data set for demonstrating main features of the package was provided by Claudia Seiler from one of a series of plate reader experiments carried out at the Institute of Hydrobiology of TU Dresden. It describes growth of three different strains of bacteria (D = Donor, R = Recipient, T = transconjugant) in dependence of a gradient of the antibiotics tetracycline.

suppressMessages(require("growthrates")) #require("growthrates")

After loading the package:

library("growthrates")

we load the data and inspect its structure with `str`

:

data(bactgrowth) str(bactgrowth)

And we can also inspect the full data set with `View(growthrates)`

or
look at the first few lines with `head`

:

```
head(bactgrowth)
```

or we can plot raw data:

library(lattice) data(bactgrowth) xyplot(value ~ time|strain+as.factor(conc), data = bactgrowth, groups = replicate, pch = 16, cex = 0.5)

Package **growthrates** can determine growth parameters from single experiments,
from a complete series of experiments, or from subsets in one step. Here we start
with an overview over the ``single subset''-methods and show then examples for
fitting growth models to the full data set. More examples can be found on the
help pages of the package.

Single data sets can be analysed with functions `fit_easylinear`

,
`fit_growthmodels`

or `fit_splines`

. As a prerequisite, single data
sets containing only one treatment have to be extracted from a
complete experiment, which can be done with function `multsiplit'. In
the following example, the full data table is first split into a list
of experiments according to a vector of criteria and then the first
experiment is extracted:

splitted.data <- multisplit(bactgrowth, c("strain", "conc", "replicate")) dat <- splitted.data[[1]]

In the next step, model fitting is done, e.g. with the "easylinear" method:

fit <- fit_easylinear(dat$time, dat$value)

This method fits segments of linear models to the log-transformed data and tries to find the maximum growth rate. Several functions exist to inspect the outcome of the model fit, e.g.:

```
summary(fit)
```

coef(fit) # exponential growth parameters rsquared(fit) # coefficient of determination (of log-transformed data) deviance(fit) # residual sum of squares of log-transformed data

Plotting can then be done either in log-scale or after re-transformation:

par(mfrow = c(1, 2)) plot(fit, log = "y") plot(fit)

and in addition to the original method of @Hall2014 it is also possible to modify the default settings of the algorithm:

fitx <- fit_easylinear(dat$time, dat$value, h = 8, quota = 0.95) plot(fit) lines(fitx, pch = "+", col = "blue")

A **parametric growth model** consists of a mathematical formula that describes
the growth of a population (e.g. `grow_logistic`

) and its parameters
(e.g. `y0`

, `mumax`

, and `K`

,). **Fitting a parametric model** is
the process of estimating an optimal parameter set that minimizes a given quality
criterion. Here we use the *method of least squares*, also known as
*ordinary least squares regression* (OLS). As most of the growth models are
non-linear, we need always a goof set of start parameters `p`

. It is wise
to choose values for start parameters carefully by considering the main
properties of the selected growth model (e.g. that the carrying capacity `K`

should be around the observed maximum of the data), or by experimentation, i.e.
plotting the model together with the data. In order to prevent unrealistic
(e.g. negative) parameter values, it is optionally possible to specify
box-constraints (`upper`

and `lower`

). For difficult problems one may consider
to change the involved model fitting algorithm from Marquardt (`"Marq"`

) to
something else, e.g. to `"L-BFGS-B"`

. Details can be found on the `?modFit`

help
page.

p <- c(y0 = 0.01, mumax = 0.2, K = 0.1) lower <- c(y0 = 1e-6, mumax = 0, K = 0) upper <- c(y0 = 0.05, mumax = 5, K = 0.5) fit1 <- fit_growthmodel(FUN = grow_logistic, p = p, dat$time, dat$value, lower = lower, upper = upper) p <- c(yi = 0.02, ya = 0.001, kw = 0.1, mumax = 0.2, K = 0.1) lower <- c(yi = 1e-6, ya = 1e-6, kw = 0, mumax = 0, K = 0) upper <- c(yi = 0.05, ya = 0.05, kw = 10, mumax = 5, K = 0.5) fit2 <- fit_growthmodel(FUN = grow_twostep, p = p, time = dat$time, y = dat$value, lower = lower, upper = upper) coef(fit1) coef(fit2) par(mfrow = c(1, 2)) plot(fit1) lines(fit2, col = "red") plot(fit1, log = "y") lines(fit2, col = "red")

In the two-step model abode, growth is described as a two-step process of adaption of inactive cells $y_i$ and logistic growth of active cells $y_a$:

$$\frac{dy_i}{dt} = - k_w \cdot y_i $$

$$\frac{dy_a}{dt} = k_w \cdot y_i + \mu_{max} \cdot y_a \cdot \left(1 - \frac{y_a + y_i}{K} \right)$$

with amount of total organisms $y = y_i + y_a$, and an adaption rate $k_w$, intrinsic growth rate $\mu_{max}$, and carrying capacity $K$. The initial abundance (normally $y_0$) is splitted in two separate values, $y_{i,0}$ and $y_{a,0}$ that are by default also fitted.

The underlying ordinary differential equation (ODE) model has no
simple analytical solution and is therefore solved numerically using a
differential solver from package **deSolve**. Here both, the model and
the solver are running in compiled code (C resp. Fortran), but it is
of course also possible to define user-specified models in R code. Details
can be found in *Part 2* of the package documentation.

Despite the fact that the above model is solved as a differential
equation, the relatively high number of parameters may need special
care, too. In such cases, package growthrates allows to fit subsets of
parameters while setting the others to fixed values. In the following,
this is done by specifying a subset without initial abundances $y_a$
and $y_i$ in `which`

:

fit3 <- fit_growthmodel(FUN = grow_twostep, p = p, time = dat$time, y = dat$value, lower = lower, upper = upper, which = c("kw", "mumax", "K")) summary(fit3) coef(fit3) plot(fit3)

We see that `summary`

shows only the fitted parameters whereas `coef`

contains the full set.

Note however, that start values need to be given in `p`

for all model
parameters, i.e. for both the fitted and the fixed ones, while `upper`

and
`lower`

bounds for the fixed parameters can be omitted.

Smoothing splines are a quick method to estimate maximum growth. The method is
called *nonparametric*, because the growth rate is directly estimated from the
smoothed data without being restricted to a specific model formula.

dat <- splitted.data[[2]] time <- dat$time y <- dat$value ## automatic smoothing with cv res <- fit_spline(time, y) par(mfrow = c(1, 2)) plot(res, log = "y") plot(res) coef(res)

Fiting multiple data sets at once is possible with functions
`all_easyliner`

, `all_growthmodels`

and `all_splines`

. Usage is
similar for all methods, and the parameters are analogous to the
single-fit methods. Both, the easy growth rates and the smooting
splines method are quite robust. In contrast to this, parametric fits
with function `all_growthmodels`

need more care and a little bit more
computational power.

Again, special emphasis should be given to the selection of good starting
points. In addition, it is possible to select an alternative
optimization algorithm, to enable additional output (`trace`

) or to
fine-tune their optimization control parameters. Nevertheless, it
should be noted that parametric models have more explanatory power and
may therefore be advantageous for basic research.

Nonlinear optimization is done with parallelized code, so multi-core computers can speed up computation.

It can be a good idea, to start with a nonparametric approach like the
smoothing spline method to get a first impression and, potentially, to
derive start parameters for a parametric model.
In the following, we show an example with the smoothing spline method. The
function uses a formula interface with the syntax:
`dependent_variable ~ independent_variable | group1 + group 2 + ...`

:

In this example, smoothness is set to a moderate value (`spar = 0.5`

).
Other values between zero and one will result in different degrees of
smoothing. If `spar`

is omitted, leave-one-out cross-validation is
used to determine smoothness automatically. This works best if the samples
over time are true replicates from independent experimental units,
instead of pseudo-replicates with potential autocorrelation.

many_spline_fits <- all_splines(value ~ time | strain + conc + replicate, data = bactgrowth, spar = 0.5) par(mfrow = c(12, 6)) par(mar = c(2.5, 4, 2, 1)) plot(many_spline_fits)

Package **growthrates** allows to fit parametric models to a series of
grouped data. The formula interface of function `all_growthmodels`

allows to include the name of the nonlinear model
(the `grow_....`

-function) and the name of the independent variable
(e.g. `time`

) as its first argument, for example as
`grow_logistic(time, parms)`

. The second argument `parms`

is a dummy
argument; its name does currently not (yet) matter. Model fitting can
make use of multiple CPU cores to speed up computation. If the
`ncores`

argument is omitted, the number of cores is automatically
detected while setting `ncores = 1`

can be useful for debugging.

In the following, let's fit a Baranyi growth model [@Baranyi1995] to the data, a model that considers the lag phase as as a period to build up ``critical substances'' needed for growth. The model is based on a system of two differential equations for which under some simplifying assumptions an analytical solution was presented.

In a first attempt, we fit all parameters of the model:

## initial parameters and box constraints p <- c(y0 = 0.03, mumax = .1, K = 0.1, h0 = 1) lower <- c(y0 = 0.001, mumax = 1e-2, K = 0.005, h0 = 0) upper <- c(y0 = 0.1, mumax = 1, K = 0.5, h0 = 10) ## fit growth models to all data using log transformed residuals many_baranyi1 <- all_growthmodels( value ~ grow_baranyi(time, parms) | strain + conc + replicate, data = bactgrowth, p = p, lower = lower, upper = upper, transform = "log", ncores = 2)

whereas in a second trial, `h0`

is fixed to a common value to avoid
that `h0`

consumes parts of the effect because of interdependency
between `h0`

and `mumax`

:

## use coefficients of first fit as new initial parameters pp <- coef(many_baranyi1) ## but set h0 to a fixed value pp[, "h0"] <- 0.65 ## re-fit models many_baranyi2 <- all_growthmodels( value ~ grow_baranyi(time, parms) | strain + conc + replicate, data = bactgrowth, p = pp, lower = lower, upper = upper, which = c("y0", "mumax", "K"), transform = "log", ncores = 2)

The result of the second fit is shown in the following figure. It may be noted that even better fits are possible with models with more parameters, e.g. @Huang2011, again at the cost that the effect of antibiotics is distributed over several correlated parameters instead of an effect of the maximum growth rate.

par(mfrow = c(12, 6)) par(mar = c(2.5, 4, 2, 1)) plot(many_baranyi2)

Dependency of growth rate on antibiotic concentration for the three strains with the spline fit and the Baranyi model:

many_spline_res <- results(many_spline_fits) many_baranyi2_res <- results(many_baranyi2) xyplot(mumax ~ log(conc+1)|strain, data = many_spline_res, layout = c(3, 1)) xyplot(mumax ~ log(conc+1)|strain, data = many_baranyi2_res, layout = c(3, 1))

Describing the observed dependency can again be approached with
nonparametric methods or parametric functional response curves, which
may be done by using a specialized package for dose-response curves,
for example package **drc** [@Ritz2005].

Many thanks to Claudia Seiler for the data set, and to the R Core Team
[@RCore2015] for developing and maintaining **R**. This documentation
was written using **knitr** [@knitr2014] and **rmarkdown**
[@rmarkdown].

**Copyright and original author:** tpetzoldt, `r Sys.Date()`

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